Papers by Shotaro Ishihara
Semantic Shift Stability: Efficient Way to Detect Performance Degradation of Word Embeddings and Pre-trained Language Models (2022.aacl-main)
Copied to clipboard
| Challenge: | Existing methods to detect time-series performance degradation of word embeddings and pre-trained language models are not efficient. |
| Approach: | They propose a way to detect time-series performance degradation by calculating the degree of semantic shift. |
| Outcome: | The proposed method detects time-series performance degradation in Japanese and English datasets. |
Fast-MIA: Efficient and Scalable Membership Inference for LLMs (2026.acl-demo)
Copied to clipboard
| Challenge: | a library for evaluating membership inference attacks against large language models (LLMs) has emerged as a crucial technique for auditing privacy risks and copyright infringement in LLMs. |
| Approach: | They propose a Python library for efficiently evaluating membership inference attacks against large language models (LLMs) they use a high-throughput batch inference via vLLM and a cross-method caching architecture that computes intermediate results once and shares them across methods. |
| Outcome: | The proposed library performs a 5 speedup in inference and a cross-method caching architecture that computes intermediate results once and shares them across methods. |